Performance Enhancement in High-Speed Contact-Mode Atomic Force Microscopy
Bibliographic record
Abstract
Atomic force microscopy is having a substantial impact on nanosciences and technologies. However, the low atomic- force-microscope (AFM) scanning speed continues to be a major obstacle that impedes the widespread adoption of AFM-based systems. This paper presents a controller design approach for constant-force contact-mode AFM operation to enhance the AFM system performance with respect to the scanning speed and the image accuracy. The purpose of the controller is to maintain a constant force between the cantilever tip and the sample surface through suitable displacement of the base end of the cantilever. Given that the sample surface profile is unknown, the difficulty in the controller design lies in attempting to regulate the contact force against an unknown and time-varying signal. To overcome this problem, it is proposed in this paper to use a two-step adaptive regulator design approach. The first step involves the use of the$Q$parameterization of stabilizing controllers to construct a set of parameterized stabilizing controllers. The second step involves tuning the$Q$parameter in the expression of stabilizing controllers so that the tuned controller converges to the desired controller needed to achieve regulation. The proposed strategy makes it possible to use small contact forces and high scanning speeds, hence improving the performance of contact-mode AFM systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".